Top-k Maximal Influential Paths in Network Data

  • Enliang Xu
  • Wynne Hsu
  • Mong Li Lee
  • Dhaval Patel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7446)


Information diffusion is a fundamental process taking place in networks. It is often possible to observe when nodes get influenced, but it is hard to directly observe the underlying network. Furthermore, in many applications, the underlying networks are implicit or even unknown. Existing works on network inference can only infer influential edges between two nodes. In this paper, we develop a method for inferring top-k maximal influential paths which can capture the dynamics of information diffusion better compared to influential edges. We define a generative influence propagation model based on the Independent Cascade Model and Linear Threshold Model, which mathematically model the spread of certain information through a network. We formalize the top-k maximal influential path inference problem and develop an efficient algorithm, called TIP, to infer the top-k maximal influential paths. TIP makes use of the properties of top-k maximal influential paths to dynamically increase the support and prune the projected databases. We evaluate the proposed algorithms on both synthetic and real world data sets. The experimental results demonstrate the effectiveness and efficiency of our method.


Information Diffusion Synthetic Dataset Database Size Frequent Node Influential Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adar, E., Adamic, L.A.: Tracking Information Epidemics in Blogspace. In: Web Intelligence, pp. 207–214 (2005)Google Scholar
  2. 2.
    Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the Influential Bloggers in a Community. In: WSDM 2008, pp. 207–218 (2008)Google Scholar
  3. 3.
    Chen, W., Wang, C., Wang, Y.: Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks. In: KDD 2010, pp. 1029–1038 (2010)Google Scholar
  4. 4.
    Chen, W., Wang, Y., Yang, S.: Efficient Influence Maximization in Social Networks. In: KDD 2009, pp. 199–208 (2009)Google Scholar
  5. 5.
    Domingos, P., Richardson, M.: Mining the Network Value of Customers. In: KDD 2001, pp. 57–66 (2001)Google Scholar
  6. 6.
    Gomez-Rodriguez, M., Leskovec, J., Krause, A.: Inferring Networks of Diffusion and Influence. In: KDD 2010, pp. 1019–1028 (2010)Google Scholar
  7. 7.
    Gruhl, D., Guha, R., Liben-nowell, D., Tomkins, A.: Information Diffusion through Blogspace. In: WWW 2004, pp. 491–501 (2004)Google Scholar
  8. 8.
    Java, A., Kolari, P., Finin, T., Oates, T.: Modeling the Spread of Influence on the Blogosphere. In: World Wide Web Conference Series (2006)Google Scholar
  9. 9.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the Spread of Influence through a Social Network. In: KDD 2003, pp. 137–146 (2003)Google Scholar
  10. 10.
    Kimura, M., Saito, K.: Tractable Models for Information Diffusion in Social Networks. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 259–271. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective Outbreak Detection in Networks. In: KDD 2007, pp. 420–429 (2007)Google Scholar
  12. 12.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the Dynamics of the News Cycle. In: KDD 2009, pp. 497–506 (2009)Google Scholar
  13. 13.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Prefixspan: Mining Sequential Patterns Efficiently by Prefix-projected Pattern Growth. In: ICDE 2001, pp. 215–224 (2001)Google Scholar
  14. 14.
    Yan, X., Han, J., Afshar, R.: Clospan: Mining Closed Sequential Patterns in Large Datasets. In: SDM 2003, pp. 166–177 (2003)Google Scholar
  15. 15.
    Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining Topic-level Influence in Heterogeneous Networks. In: CIKM 2010, pp. 199–208 (2010)Google Scholar
  16. 16.
    Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of Influence Networks. In: KDD 2011, pp. 529–537 (2011)Google Scholar
  17. 17.
    Narayanam, R., Narahari, Y.: A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks. IEEE T. Automation Science and Engineering 8(1), 130–147 (2011)CrossRefGoogle Scholar
  18. 18.
    Richardson, M., Domingos, P.: Mining Knowledge-Sharing Sites for Viral Marketing. In: KDD 2002, pp. 61–70 (2002)Google Scholar
  19. 19.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social Influence Analysis in Large-scale Networks. In: KDD 2009, pp. 807–816 (2009)Google Scholar
  20. 20.
    Watkins, R., Eagleson, S., Beckett, S., Garner, G., Veenendaal, B., Wright, G., Plant, A.: Using GIS to Create Synthetic Disease Outbreaks. BMC Medical Informatics and Decision Making 7(1), 4 (2007)CrossRefGoogle Scholar
  21. 21.
    Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Mining Sequences with Temporal Annotations. In: SAC 2006, pp. 593–597 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Enliang Xu
    • 1
  • Wynne Hsu
    • 1
  • Mong Li Lee
    • 1
  • Dhaval Patel
    • 1
  1. 1.School of ComputingNational University of SingaporeSingapore

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